04.Data_Wrangling_2of3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python in Data Science"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling with Pandas \n",
    "### Part 2 of 3 - Reshaping the Data Frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling 2\n",
    "\n",
    "- Applying formulas\n",
    "- Reshaping\n",
    "  - Long table to wide\n",
    "  - Wide table to long\n",
    "- Pivots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = {'year': [2010, 2011, 2012, 2011, 2012, 2010, 2011, 2012],\n",
    "        'team': ['Bears', 'Bears', 'Bears', 'Packers', 'Packers', 'Lions',\n",
    "                 'Lions', 'Lions'],\n",
    "        'wins': [11, 8, 10, 15, 11, 6, 10, 4],\n",
    "        'losses': [5, 8, 6, 1, 5, 10, 6, 12]}\n",
    "\n",
    "\n",
    "football = pd.DataFrame(data)\n",
    "football"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1\n",
    "\n",
    "Create a summary for `Packers` (create a `DataFrame` for that team)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "football[football.team == \"Packers\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "football[football.team == \"Packers\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2\n",
    "\n",
    "Add the column `games_played` to the DataFrame `football` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "football['games_played'] = football.wins + football.losses\n",
    "football"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3\n",
    "\n",
    "Add the column `percentage_games_won` to the DataFrame `football` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "football['percentage_games_won'] = 100.0 * football.wins / football.games_played\n",
    "football"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 4\n",
    "\n",
    "Display the data for the  `Packers` team only for even years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "football[(football.year % 2 == 0) & (football.team == \"Packers\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tidy Data\n",
    "\n",
    "- __Each variable you measure should be in one column.__\n",
    "- __Each different observation of that variable should be in a different row.__\n",
    "- There should be one table for each \"kind\" of variable.\n",
    "- If you have multiple tables, they should include a column in the table that allows them to be linked."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "## Applying formulas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "df = pd.DataFrame(np.random.randint(1,100, 80).reshape(-1, 10))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def highlight_max(s):\n",
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    "    return ['background-color: blue' if v else '' for v in is_max]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<pandas.io.formats.style.Styler at 0x248ce8e0d00>"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.style.apply(highlight_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >0</th>\n",
       "      <th class=\"col_heading level0 col1\" >1</th>\n",
       "      <th class=\"col_heading level0 col2\" >2</th>\n",
       "      <th class=\"col_heading level0 col3\" >3</th>\n",
       "      <th class=\"col_heading level0 col4\" >4</th>\n",
       "      <th class=\"col_heading level0 col5\" >5</th>\n",
       "      <th class=\"col_heading level0 col6\" >6</th>\n",
       "      <th class=\"col_heading level0 col7\" >7</th>\n",
       "      <th class=\"col_heading level0 col8\" >8</th>\n",
       "      <th class=\"col_heading level0 col9\" >9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_46e64_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_46e64_row0_col0\" class=\"data row0 col0\" >16</td>\n",
       "      <td id=\"T_46e64_row0_col1\" class=\"data row0 col1\" >94</td>\n",
       "      <td id=\"T_46e64_row0_col2\" class=\"data row0 col2\" >2</td>\n",
       "      <td id=\"T_46e64_row0_col3\" class=\"data row0 col3\" >97</td>\n",
       "      <td id=\"T_46e64_row0_col4\" class=\"data row0 col4\" >25</td>\n",
       "      <td id=\"T_46e64_row0_col5\" class=\"data row0 col5\" >37</td>\n",
       "      <td id=\"T_46e64_row0_col6\" class=\"data row0 col6\" >41</td>\n",
       "      <td id=\"T_46e64_row0_col7\" class=\"data row0 col7\" >84</td>\n",
       "      <td id=\"T_46e64_row0_col8\" class=\"data row0 col8\" >31</td>\n",
       "      <td id=\"T_46e64_row0_col9\" class=\"data row0 col9\" >42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_46e64_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_46e64_row1_col0\" class=\"data row1 col0\" >31</td>\n",
       "      <td id=\"T_46e64_row1_col1\" class=\"data row1 col1\" >32</td>\n",
       "      <td id=\"T_46e64_row1_col2\" class=\"data row1 col2\" >62</td>\n",
       "      <td id=\"T_46e64_row1_col3\" class=\"data row1 col3\" >58</td>\n",
       "      <td id=\"T_46e64_row1_col4\" class=\"data row1 col4\" >61</td>\n",
       "      <td id=\"T_46e64_row1_col5\" class=\"data row1 col5\" >59</td>\n",
       "      <td id=\"T_46e64_row1_col6\" class=\"data row1 col6\" >57</td>\n",
       "      <td id=\"T_46e64_row1_col7\" class=\"data row1 col7\" >79</td>\n",
       "      <td id=\"T_46e64_row1_col8\" class=\"data row1 col8\" >80</td>\n",
       "      <td id=\"T_46e64_row1_col9\" class=\"data row1 col9\" >9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_46e64_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_46e64_row2_col0\" class=\"data row2 col0\" >76</td>\n",
       "      <td id=\"T_46e64_row2_col1\" class=\"data row2 col1\" >69</td>\n",
       "      <td id=\"T_46e64_row2_col2\" class=\"data row2 col2\" >94</td>\n",
       "      <td id=\"T_46e64_row2_col3\" class=\"data row2 col3\" >8</td>\n",
       "      <td id=\"T_46e64_row2_col4\" class=\"data row2 col4\" >16</td>\n",
       "      <td id=\"T_46e64_row2_col5\" class=\"data row2 col5\" >79</td>\n",
       "      <td id=\"T_46e64_row2_col6\" class=\"data row2 col6\" >42</td>\n",
       "      <td id=\"T_46e64_row2_col7\" class=\"data row2 col7\" >44</td>\n",
       "      <td id=\"T_46e64_row2_col8\" class=\"data row2 col8\" >83</td>\n",
       "      <td id=\"T_46e64_row2_col9\" class=\"data row2 col9\" >72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_46e64_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_46e64_row3_col0\" class=\"data row3 col0\" >11</td>\n",
       "      <td id=\"T_46e64_row3_col1\" class=\"data row3 col1\" >5</td>\n",
       "      <td id=\"T_46e64_row3_col2\" class=\"data row3 col2\" >15</td>\n",
       "      <td id=\"T_46e64_row3_col3\" class=\"data row3 col3\" >25</td>\n",
       "      <td id=\"T_46e64_row3_col4\" class=\"data row3 col4\" >37</td>\n",
       "      <td id=\"T_46e64_row3_col5\" class=\"data row3 col5\" >95</td>\n",
       "      <td id=\"T_46e64_row3_col6\" class=\"data row3 col6\" >57</td>\n",
       "      <td id=\"T_46e64_row3_col7\" class=\"data row3 col7\" >64</td>\n",
       "      <td id=\"T_46e64_row3_col8\" class=\"data row3 col8\" >78</td>\n",
       "      <td id=\"T_46e64_row3_col9\" class=\"data row3 col9\" >85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_46e64_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_46e64_row4_col0\" class=\"data row4 col0\" >69</td>\n",
       "      <td id=\"T_46e64_row4_col1\" class=\"data row4 col1\" >48</td>\n",
       "      <td id=\"T_46e64_row4_col2\" class=\"data row4 col2\" >8</td>\n",
       "      <td id=\"T_46e64_row4_col3\" class=\"data row4 col3\" >68</td>\n",
       "      <td id=\"T_46e64_row4_col4\" class=\"data row4 col4\" >93</td>\n",
       "      <td id=\"T_46e64_row4_col5\" class=\"data row4 col5\" >35</td>\n",
       "      <td id=\"T_46e64_row4_col6\" class=\"data row4 col6\" >16</td>\n",
       "      <td id=\"T_46e64_row4_col7\" class=\"data row4 col7\" >89</td>\n",
       "      <td id=\"T_46e64_row4_col8\" class=\"data row4 col8\" >27</td>\n",
       "      <td id=\"T_46e64_row4_col9\" class=\"data row4 col9\" >31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_46e64_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_46e64_row5_col0\" class=\"data row5 col0\" >95</td>\n",
       "      <td id=\"T_46e64_row5_col1\" class=\"data row5 col1\" >99</td>\n",
       "      <td id=\"T_46e64_row5_col2\" class=\"data row5 col2\" >24</td>\n",
       "      <td id=\"T_46e64_row5_col3\" class=\"data row5 col3\" >84</td>\n",
       "      <td id=\"T_46e64_row5_col4\" class=\"data row5 col4\" >40</td>\n",
       "      <td id=\"T_46e64_row5_col5\" class=\"data row5 col5\" >60</td>\n",
       "      <td id=\"T_46e64_row5_col6\" class=\"data row5 col6\" >16</td>\n",
       "      <td id=\"T_46e64_row5_col7\" class=\"data row5 col7\" >82</td>\n",
       "      <td id=\"T_46e64_row5_col8\" class=\"data row5 col8\" >74</td>\n",
       "      <td id=\"T_46e64_row5_col9\" class=\"data row5 col9\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_46e64_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_46e64_row6_col0\" class=\"data row6 col0\" >10</td>\n",
       "      <td id=\"T_46e64_row6_col1\" class=\"data row6 col1\" >52</td>\n",
       "      <td id=\"T_46e64_row6_col2\" class=\"data row6 col2\" >75</td>\n",
       "      <td id=\"T_46e64_row6_col3\" class=\"data row6 col3\" >18</td>\n",
       "      <td id=\"T_46e64_row6_col4\" class=\"data row6 col4\" >31</td>\n",
       "      <td id=\"T_46e64_row6_col5\" class=\"data row6 col5\" >81</td>\n",
       "      <td id=\"T_46e64_row6_col6\" class=\"data row6 col6\" >62</td>\n",
       "      <td id=\"T_46e64_row6_col7\" class=\"data row6 col7\" >28</td>\n",
       "      <td id=\"T_46e64_row6_col8\" class=\"data row6 col8\" >77</td>\n",
       "      <td id=\"T_46e64_row6_col9\" class=\"data row6 col9\" >17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_46e64_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_46e64_row7_col0\" class=\"data row7 col0\" >42</td>\n",
       "      <td id=\"T_46e64_row7_col1\" class=\"data row7 col1\" >25</td>\n",
       "      <td id=\"T_46e64_row7_col2\" class=\"data row7 col2\" >50</td>\n",
       "      <td id=\"T_46e64_row7_col3\" class=\"data row7 col3\" >42</td>\n",
       "      <td id=\"T_46e64_row7_col4\" class=\"data row7 col4\" >12</td>\n",
       "      <td id=\"T_46e64_row7_col5\" class=\"data row7 col5\" >68</td>\n",
       "      <td id=\"T_46e64_row7_col6\" class=\"data row7 col6\" >19</td>\n",
       "      <td id=\"T_46e64_row7_col7\" class=\"data row7 col7\" >59</td>\n",
       "      <td id=\"T_46e64_row7_col8\" class=\"data row7 col8\" >33</td>\n",
       "      <td id=\"T_46e64_row7_col9\" class=\"data row7 col9\" >34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x248d2b32e20>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.style.apply(highlight_max, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    95\n",
       "1    99\n",
       "2    94\n",
       "3    97\n",
       "4    93\n",
       "5    95\n",
       "6    62\n",
       "7    89\n",
       "8    83\n",
       "9    85\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(max,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_4117e_row0_col3, #T_4117e_row1_col8, #T_4117e_row2_col2, #T_4117e_row3_col5, #T_4117e_row4_col4, #T_4117e_row5_col1, #T_4117e_row6_col5, #T_4117e_row7_col5 {\n",
       "  background-color: blue;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4117e_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >0</th>\n",
       "      <th class=\"col_heading level0 col1\" >1</th>\n",
       "      <th class=\"col_heading level0 col2\" >2</th>\n",
       "      <th class=\"col_heading level0 col3\" >3</th>\n",
       "      <th class=\"col_heading level0 col4\" >4</th>\n",
       "      <th class=\"col_heading level0 col5\" >5</th>\n",
       "      <th class=\"col_heading level0 col6\" >6</th>\n",
       "      <th class=\"col_heading level0 col7\" >7</th>\n",
       "      <th class=\"col_heading level0 col8\" >8</th>\n",
       "      <th class=\"col_heading level0 col9\" >9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4117e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_4117e_row0_col0\" class=\"data row0 col0\" >16</td>\n",
       "      <td id=\"T_4117e_row0_col1\" class=\"data row0 col1\" >94</td>\n",
       "      <td id=\"T_4117e_row0_col2\" class=\"data row0 col2\" >2</td>\n",
       "      <td id=\"T_4117e_row0_col3\" class=\"data row0 col3\" >97</td>\n",
       "      <td id=\"T_4117e_row0_col4\" class=\"data row0 col4\" >25</td>\n",
       "      <td id=\"T_4117e_row0_col5\" class=\"data row0 col5\" >37</td>\n",
       "      <td id=\"T_4117e_row0_col6\" class=\"data row0 col6\" >41</td>\n",
       "      <td id=\"T_4117e_row0_col7\" class=\"data row0 col7\" >84</td>\n",
       "      <td id=\"T_4117e_row0_col8\" class=\"data row0 col8\" >31</td>\n",
       "      <td id=\"T_4117e_row0_col9\" class=\"data row0 col9\" >42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4117e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_4117e_row1_col0\" class=\"data row1 col0\" >31</td>\n",
       "      <td id=\"T_4117e_row1_col1\" class=\"data row1 col1\" >32</td>\n",
       "      <td id=\"T_4117e_row1_col2\" class=\"data row1 col2\" >62</td>\n",
       "      <td id=\"T_4117e_row1_col3\" class=\"data row1 col3\" >58</td>\n",
       "      <td id=\"T_4117e_row1_col4\" class=\"data row1 col4\" >61</td>\n",
       "      <td id=\"T_4117e_row1_col5\" class=\"data row1 col5\" >59</td>\n",
       "      <td id=\"T_4117e_row1_col6\" class=\"data row1 col6\" >57</td>\n",
       "      <td id=\"T_4117e_row1_col7\" class=\"data row1 col7\" >79</td>\n",
       "      <td id=\"T_4117e_row1_col8\" class=\"data row1 col8\" >80</td>\n",
       "      <td id=\"T_4117e_row1_col9\" class=\"data row1 col9\" >9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4117e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_4117e_row2_col0\" class=\"data row2 col0\" >76</td>\n",
       "      <td id=\"T_4117e_row2_col1\" class=\"data row2 col1\" >69</td>\n",
       "      <td id=\"T_4117e_row2_col2\" class=\"data row2 col2\" >94</td>\n",
       "      <td id=\"T_4117e_row2_col3\" class=\"data row2 col3\" >8</td>\n",
       "      <td id=\"T_4117e_row2_col4\" class=\"data row2 col4\" >16</td>\n",
       "      <td id=\"T_4117e_row2_col5\" class=\"data row2 col5\" >79</td>\n",
       "      <td id=\"T_4117e_row2_col6\" class=\"data row2 col6\" >42</td>\n",
       "      <td id=\"T_4117e_row2_col7\" class=\"data row2 col7\" >44</td>\n",
       "      <td id=\"T_4117e_row2_col8\" class=\"data row2 col8\" >83</td>\n",
       "      <td id=\"T_4117e_row2_col9\" class=\"data row2 col9\" >72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4117e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_4117e_row3_col0\" class=\"data row3 col0\" >11</td>\n",
       "      <td id=\"T_4117e_row3_col1\" class=\"data row3 col1\" >5</td>\n",
       "      <td id=\"T_4117e_row3_col2\" class=\"data row3 col2\" >15</td>\n",
       "      <td id=\"T_4117e_row3_col3\" class=\"data row3 col3\" >25</td>\n",
       "      <td id=\"T_4117e_row3_col4\" class=\"data row3 col4\" >37</td>\n",
       "      <td id=\"T_4117e_row3_col5\" class=\"data row3 col5\" >95</td>\n",
       "      <td id=\"T_4117e_row3_col6\" class=\"data row3 col6\" >57</td>\n",
       "      <td id=\"T_4117e_row3_col7\" class=\"data row3 col7\" >64</td>\n",
       "      <td id=\"T_4117e_row3_col8\" class=\"data row3 col8\" >78</td>\n",
       "      <td id=\"T_4117e_row3_col9\" class=\"data row3 col9\" >85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4117e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_4117e_row4_col0\" class=\"data row4 col0\" >69</td>\n",
       "      <td id=\"T_4117e_row4_col1\" class=\"data row4 col1\" >48</td>\n",
       "      <td id=\"T_4117e_row4_col2\" class=\"data row4 col2\" >8</td>\n",
       "      <td id=\"T_4117e_row4_col3\" class=\"data row4 col3\" >68</td>\n",
       "      <td id=\"T_4117e_row4_col4\" class=\"data row4 col4\" >93</td>\n",
       "      <td id=\"T_4117e_row4_col5\" class=\"data row4 col5\" >35</td>\n",
       "      <td id=\"T_4117e_row4_col6\" class=\"data row4 col6\" >16</td>\n",
       "      <td id=\"T_4117e_row4_col7\" class=\"data row4 col7\" >89</td>\n",
       "      <td id=\"T_4117e_row4_col8\" class=\"data row4 col8\" >27</td>\n",
       "      <td id=\"T_4117e_row4_col9\" class=\"data row4 col9\" >31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4117e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_4117e_row5_col0\" class=\"data row5 col0\" >95</td>\n",
       "      <td id=\"T_4117e_row5_col1\" class=\"data row5 col1\" >99</td>\n",
       "      <td id=\"T_4117e_row5_col2\" class=\"data row5 col2\" >24</td>\n",
       "      <td id=\"T_4117e_row5_col3\" class=\"data row5 col3\" >84</td>\n",
       "      <td id=\"T_4117e_row5_col4\" class=\"data row5 col4\" >40</td>\n",
       "      <td id=\"T_4117e_row5_col5\" class=\"data row5 col5\" >60</td>\n",
       "      <td id=\"T_4117e_row5_col6\" class=\"data row5 col6\" >16</td>\n",
       "      <td id=\"T_4117e_row5_col7\" class=\"data row5 col7\" >82</td>\n",
       "      <td id=\"T_4117e_row5_col8\" class=\"data row5 col8\" >74</td>\n",
       "      <td id=\"T_4117e_row5_col9\" class=\"data row5 col9\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4117e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_4117e_row6_col0\" class=\"data row6 col0\" >10</td>\n",
       "      <td id=\"T_4117e_row6_col1\" class=\"data row6 col1\" >52</td>\n",
       "      <td id=\"T_4117e_row6_col2\" class=\"data row6 col2\" >75</td>\n",
       "      <td id=\"T_4117e_row6_col3\" class=\"data row6 col3\" >18</td>\n",
       "      <td id=\"T_4117e_row6_col4\" class=\"data row6 col4\" >31</td>\n",
       "      <td id=\"T_4117e_row6_col5\" class=\"data row6 col5\" >81</td>\n",
       "      <td id=\"T_4117e_row6_col6\" class=\"data row6 col6\" >62</td>\n",
       "      <td id=\"T_4117e_row6_col7\" class=\"data row6 col7\" >28</td>\n",
       "      <td id=\"T_4117e_row6_col8\" class=\"data row6 col8\" >77</td>\n",
       "      <td id=\"T_4117e_row6_col9\" class=\"data row6 col9\" >17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4117e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_4117e_row7_col0\" class=\"data row7 col0\" >42</td>\n",
       "      <td id=\"T_4117e_row7_col1\" class=\"data row7 col1\" >25</td>\n",
       "      <td id=\"T_4117e_row7_col2\" class=\"data row7 col2\" >50</td>\n",
       "      <td id=\"T_4117e_row7_col3\" class=\"data row7 col3\" >42</td>\n",
       "      <td id=\"T_4117e_row7_col4\" class=\"data row7 col4\" >12</td>\n",
       "      <td id=\"T_4117e_row7_col5\" class=\"data row7 col5\" >68</td>\n",
       "      <td id=\"T_4117e_row7_col6\" class=\"data row7 col6\" >19</td>\n",
       "      <td id=\"T_4117e_row7_col7\" class=\"data row7 col7\" >59</td>\n",
       "      <td id=\"T_4117e_row7_col8\" class=\"data row7 col8\" >33</td>\n",
       "      <td id=\"T_4117e_row7_col9\" class=\"data row7 col9\" >34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x248d2b398e0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.style.apply(highlight_max, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    97\n",
       "1    80\n",
       "2    94\n",
       "3    95\n",
       "4    93\n",
       "5    99\n",
       "6    81\n",
       "7    68\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(max,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>6</th>\n",
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       "      <td>75</td>\n",
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       "      <td>81</td>\n",
       "      <td>62</td>\n",
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       "    0   1   2   3   4   5   6   7   8   9  max\n",
       "0  16  94   2  97  25  37  41  84  31  42   97\n",
       "1  31  32  62  58  61  59  57  79  80   9   80\n",
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       "6  10  52  75  18  31  81  62  28  77  17   81\n",
       "7  42  25  50  42  12  68  19  59  33  34   68"
      ]
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     "execution_count": 10,
     "metadata": {},
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    }
   ],
   "source": [
    "df[\"max\"] = df.apply(max,axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>16</td>\n",
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       "      <td>99</td>\n",
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       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>52</td>\n",
       "      <td>75</td>\n",
       "      <td>18</td>\n",
       "      <td>31</td>\n",
       "      <td>81</td>\n",
       "      <td>62</td>\n",
       "      <td>28</td>\n",
       "      <td>77</td>\n",
       "      <td>17</td>\n",
       "      <td>81</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>42</td>\n",
       "      <td>25</td>\n",
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       "      <td>42</td>\n",
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       "      <td>68</td>\n",
       "      <td>19</td>\n",
       "      <td>59</td>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
       "      <td>68</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>95</td>\n",
       "      <td>99</td>\n",
       "      <td>94</td>\n",
       "      <td>97</td>\n",
       "      <td>93</td>\n",
       "      <td>95</td>\n",
       "      <td>62</td>\n",
       "      <td>89</td>\n",
       "      <td>83</td>\n",
       "      <td>85</td>\n",
       "      <td>99</td>\n",
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       "</table>\n",
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      "text/plain": [
       "    0   1   2   3   4   5   6   7   8   9  max\n",
       "0  16  94   2  97  25  37  41  84  31  42   97\n",
       "1  31  32  62  58  61  59  57  79  80   9   80\n",
       "2  76  69  94   8  16  79  42  44  83  72   94\n",
       "3  11   5  15  25  37  95  57  64  78  85   95\n",
       "4  69  48   8  68  93  35  16  89  27  31   93\n",
       "5  95  99  24  84  40  60  16  82  74   5   99\n",
       "6  10  52  75  18  31  81  62  28  77  17   81\n",
       "7  42  25  50  42  12  68  19  59  33  34   68\n",
       "8  95  99  94  97  93  95  62  89  83  85   99"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.append(df.apply(max,axis=0), ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying formulas to cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>9409</td>\n",
       "      <td>625</td>\n",
       "      <td>1369</td>\n",
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       "      <td>961</td>\n",
       "      <td>1764</td>\n",
       "      <td>9409</td>\n",
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       "      <th>1</th>\n",
       "      <td>961</td>\n",
       "      <td>1024</td>\n",
       "      <td>3844</td>\n",
       "      <td>3364</td>\n",
       "      <td>3721</td>\n",
       "      <td>3481</td>\n",
       "      <td>3249</td>\n",
       "      <td>6241</td>\n",
       "      <td>6400</td>\n",
       "      <td>81</td>\n",
       "      <td>6400</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5776</td>\n",
       "      <td>4761</td>\n",
       "      <td>8836</td>\n",
       "      <td>64</td>\n",
       "      <td>256</td>\n",
       "      <td>6241</td>\n",
       "      <td>1764</td>\n",
       "      <td>1936</td>\n",
       "      <td>6889</td>\n",
       "      <td>5184</td>\n",
       "      <td>8836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>121</td>\n",
       "      <td>25</td>\n",
       "      <td>225</td>\n",
       "      <td>625</td>\n",
       "      <td>1369</td>\n",
       "      <td>9025</td>\n",
       "      <td>3249</td>\n",
       "      <td>4096</td>\n",
       "      <td>6084</td>\n",
       "      <td>7225</td>\n",
       "      <td>9025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4761</td>\n",
       "      <td>2304</td>\n",
       "      <td>64</td>\n",
       "      <td>4624</td>\n",
       "      <td>8649</td>\n",
       "      <td>1225</td>\n",
       "      <td>256</td>\n",
       "      <td>7921</td>\n",
       "      <td>729</td>\n",
       "      <td>961</td>\n",
       "      <td>8649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9025</td>\n",
       "      <td>9801</td>\n",
       "      <td>576</td>\n",
       "      <td>7056</td>\n",
       "      <td>1600</td>\n",
       "      <td>3600</td>\n",
       "      <td>256</td>\n",
       "      <td>6724</td>\n",
       "      <td>5476</td>\n",
       "      <td>25</td>\n",
       "      <td>9801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100</td>\n",
       "      <td>2704</td>\n",
       "      <td>5625</td>\n",
       "      <td>324</td>\n",
       "      <td>961</td>\n",
       "      <td>6561</td>\n",
       "      <td>3844</td>\n",
       "      <td>784</td>\n",
       "      <td>5929</td>\n",
       "      <td>289</td>\n",
       "      <td>6561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1764</td>\n",
       "      <td>625</td>\n",
       "      <td>2500</td>\n",
       "      <td>1764</td>\n",
       "      <td>144</td>\n",
       "      <td>4624</td>\n",
       "      <td>361</td>\n",
       "      <td>3481</td>\n",
       "      <td>1089</td>\n",
       "      <td>1156</td>\n",
       "      <td>4624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0     1     2     3     4     5     6     7     8     9   max\n",
       "0   256  8836     4  9409   625  1369  1681  7056   961  1764  9409\n",
       "1   961  1024  3844  3364  3721  3481  3249  6241  6400    81  6400\n",
       "2  5776  4761  8836    64   256  6241  1764  1936  6889  5184  8836\n",
       "3   121    25   225   625  1369  9025  3249  4096  6084  7225  9025\n",
       "4  4761  2304    64  4624  8649  1225   256  7921   729   961  8649\n",
       "5  9025  9801   576  7056  1600  3600   256  6724  5476    25  9801\n",
       "6   100  2704  5625   324   961  6561  3844   784  5929   289  6561\n",
       "7  1764   625  2500  1764   144  4624   361  3481  1089  1156  4624"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def square(x):\n",
    "    return x*x\n",
    "\n",
    "\n",
    "df.applymap(square)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# Reshaping\n",
    "  - ## Wide table to long\n",
    "  - ## __Each variable you measure should be in one column.__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob D.</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Korzycki M.</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Student Sports Literature Maths\n",
       "0  Kowalski J.      A          B     A\n",
       "1       Bob D.      B          B     C\n",
       "2  Korzycki M.      D          D     D"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Student': {0: 'Kowalski J.', 1: 'Bob D.', 2: 'Korzycki M.'},\n",
    "                   'Sports': {0: \"A\", 1: \"B\", 2: \"D\"},\n",
    "                   'Literature': {0: \"B\", 1: \"B\", 2: \"D\"},\n",
    "                   'Maths': {0: \"A\", 1: \"C\", 2: \"D\"}})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>Sports</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob D.</td>\n",
       "      <td>Sports</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Korzycki M.</td>\n",
       "      <td>Sports</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>Maths</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bob D.</td>\n",
       "      <td>Maths</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Korzycki M.</td>\n",
       "      <td>Maths</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>Literature</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bob D.</td>\n",
       "      <td>Literature</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Korzycki M.</td>\n",
       "      <td>Literature</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Student    variable value\n",
       "0  Kowalski J.      Sports     A\n",
       "1       Bob D.      Sports     B\n",
       "2  Korzycki M.      Sports     D\n",
       "3  Kowalski J.       Maths     A\n",
       "4       Bob D.       Maths     C\n",
       "5  Korzycki M.       Maths     D\n",
       "6  Kowalski J.  Literature     B\n",
       "7       Bob D.  Literature     B\n",
       "8  Korzycki M.  Literature     D"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.melt(df, id_vars=['Student'], value_vars=['Sports', 'Maths', 'Literature'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>Kowalski J.</td>\n",
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       "      <td>A</td>\n",
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       "      <td>Kowalski J.</td>\n",
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       "      <th>4</th>\n",
       "      <td>Bob D.</td>\n",
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       "      <td>Korzycki M.</td>\n",
       "      <td>Maths</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>Literature</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bob D.</td>\n",
       "      <td>Literature</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Korzycki M.</td>\n",
       "      <td>Literature</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Student     Subject Grade\n",
       "0  Kowalski J.      Sports     A\n",
       "1       Bob D.      Sports     B\n",
       "2  Korzycki M.      Sports     D\n",
       "3  Kowalski J.       Maths     A\n",
       "4       Bob D.       Maths     C\n",
       "5  Korzycki M.       Maths     D\n",
       "6  Kowalski J.  Literature     B\n",
       "7       Bob D.  Literature     B\n",
       "8  Korzycki M.  Literature     D"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.melt(df, id_vars=['Student'], value_vars=['Sports', 'Maths', 'Literature'],\n",
    "       var_name='Subject', value_name='Grade')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Korzycki M.</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>Sports</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>Maths</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>Literature</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Student     Subject Grade\n",
       "1       Bob D.      Sports     B\n",
       "4       Bob D.       Maths     C\n",
       "7       Bob D.  Literature     B\n",
       "2  Korzycki M.      Sports     D\n",
       "5  Korzycki M.       Maths     D\n",
       "8  Korzycki M.  Literature     D\n",
       "0  Kowalski J.      Sports     A\n",
       "3  Kowalski J.       Maths     A\n",
       "6  Kowalski J.  Literature     B"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.sort_values('Student')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>Kowalski J.</td>\n",
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       "      <td>Bob D.</td>\n",
       "      <td>Sports</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Korzycki M.</td>\n",
       "      <td>Sports</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>Sports</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Student     Subject Grade\n",
       "7       Bob D.  Literature     B\n",
       "8  Korzycki M.  Literature     D\n",
       "6  Kowalski J.  Literature     B\n",
       "4       Bob D.       Maths     C\n",
       "5  Korzycki M.       Maths     D\n",
       "3  Kowalski J.       Maths     A\n",
       "1       Bob D.      Sports     B\n",
       "2  Korzycki M.      Sports     D\n",
       "0  Kowalski J.      Sports     A"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.sort_values([ 'Subject', 'Student'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>Korzycki M.</td>\n",
       "      <td>Sports</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>Literature</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>Maths</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>Sports</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index      Student     Subject Grade\n",
       "0      7       Bob D.  Literature     B\n",
       "1      4       Bob D.       Maths     C\n",
       "2      1       Bob D.      Sports     B\n",
       "3      8  Korzycki M.  Literature     D\n",
       "4      5  Korzycki M.       Maths     D\n",
       "5      2  Korzycki M.      Sports     D\n",
       "6      6  Kowalski J.  Literature     B\n",
       "7      3  Kowalski J.       Maths     A\n",
       "8      0  Kowalski J.      Sports     A"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.sort_values(['Student', 'Subject']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>8</th>\n",
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       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Korzycki M.</td>\n",
       "      <td>Maths</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Korzycki M.</td>\n",
       "      <td>Sports</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>Literature</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>Maths</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>Sports</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Student     Subject Grade\n",
       "7       Bob D.  Literature     B\n",
       "4       Bob D.       Maths     C\n",
       "1       Bob D.      Sports     B\n",
       "8  Korzycki M.  Literature     D\n",
       "5  Korzycki M.       Maths     D\n",
       "2  Korzycki M.      Sports     D\n",
       "6  Kowalski J.  Literature     B\n",
       "3  Kowalski J.       Maths     A\n",
       "0  Kowalski J.      Sports     A"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.sort_values(['Student', 'Subject'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Student</th>\n",
       "      <th>Subject</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>Sports</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob D.</td>\n",
       "      <td>Sports</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Korzycki M.</td>\n",
       "      <td>Sports</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>Maths</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bob D.</td>\n",
       "      <td>Maths</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Korzycki M.</td>\n",
       "      <td>Maths</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kowalski J.</td>\n",
       "      <td>Literature</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bob D.</td>\n",
       "      <td>Literature</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Korzycki M.</td>\n",
       "      <td>Literature</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Student     Subject Grade\n",
       "0  Kowalski J.      Sports     A\n",
       "1       Bob D.      Sports     B\n",
       "2  Korzycki M.      Sports     D\n",
       "3  Kowalski J.       Maths     A\n",
       "4       Bob D.       Maths     C\n",
       "5  Korzycki M.       Maths     D\n",
       "6  Kowalski J.  Literature     B\n",
       "7       Bob D.  Literature     B\n",
       "8  Korzycki M.  Literature     D"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.sort_values(['Student', 'Subject']).sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Reshaping\n",
    "  - ## Long table to wide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({ 'foo': ['one', 'one', 'one', 'two', 'two', 'two'],\n",
    "                    'bar': ['A', 'B', 'C', 'A', 'B', 'C'],\n",
    "                    'baz': [1, 2, 3, 4, 5, 6], \n",
    "                    'zoo': ['x', 'y', 'z', 'q', 'w', 't']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>foo</th>\n",
       "      <th>bar</th>\n",
       "      <th>baz</th>\n",
       "      <th>zoo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>C</td>\n",
       "      <td>6</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   foo bar  baz zoo\n",
       "0  one   A    1   x\n",
       "1  one   B    2   y\n",
       "2  one   C    3   z\n",
       "3  two   A    4   q\n",
       "4  two   B    5   w\n",
       "5  two   C    6   t"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Long to Wide\n",
    "![title](img/pivot.png)\n",
    "\n",
    "Źródło: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>bar</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "bar  A  B  C\n",
       "foo         \n",
       "one  1  2  3\n",
       "two  4  5  6"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot(index='foo', columns='bar', values='baz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">baz</th>\n",
       "      <th colspan=\"3\" halign=\"left\">zoo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>q</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    baz       zoo      \n",
       "bar   A  B  C   A  B  C\n",
       "foo                    \n",
       "one   1  2  3   x  y  z\n",
       "two   4  5  6   q  w  t"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rolling financial forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>foo</th>\n",
       "      <th>bar</th>\n",
       "      <th>baz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>D</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   foo bar  baz\n",
       "0  one   A    1\n",
       "1  one   B    2\n",
       "2  two   C    3\n",
       "3  two   D    4"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_e = pd.DataFrame({\"foo\": ['one', 'one', 'two', 'two'],\n",
    "                   \"bar\": ['A', 'B', 'C', 'D'],\n",
    "                   \"baz\": [1, 2, 3, 4]})\n",
    "df_e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>bar</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "bar    A    B    C    D\n",
       "foo                    \n",
       "one  1.0  2.0  NaN  NaN\n",
       "two  NaN  NaN  3.0  4.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_e.pivot(index='foo', columns='bar', values='baz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>foo</th>\n",
       "      <th>bar</th>\n",
       "      <th>baz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   foo bar  baz\n",
       "0  one   A    1\n",
       "1  one   A    2\n",
       "2  two   B    3\n",
       "3  two   C    4"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_e = pd.DataFrame({\"foo\": ['one', 'one', 'two', 'two'],\n",
    "                   \"bar\": ['A', 'A', 'B', 'C'],\n",
    "                   \"baz\": [1, 2, 3, 4]})\n",
    "df_e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Index contains duplicate entries, cannot reshape",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-30-750f58dc3008>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf_e\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpivot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'foo'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'bar'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'baz'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mpivot\u001b[1;34m(self, index, columns, values)\u001b[0m\n\u001b[0;32m   7786\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpivot\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpivot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7787\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 7788\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mpivot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   7789\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7790\u001b[0m     _shared_docs[\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\pandas\\core\\reshape\\pivot.py\u001b[0m in \u001b[0;36mpivot\u001b[1;34m(data, index, columns, values)\u001b[0m\n\u001b[0;32m    515\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    516\u001b[0m             \u001b[0mindexed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor_sliced\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmultiindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 517\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mindexed\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns_listlike\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    518\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    519\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36munstack\u001b[1;34m(self, level, fill_value)\u001b[0m\n\u001b[0;32m   4079\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0munstack\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4080\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4081\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0munstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4082\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4083\u001b[0m     \u001b[1;31m# ----------------------------------------------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\pandas\\core\\reshape\\reshape.py\u001b[0m in \u001b[0;36munstack\u001b[1;34m(obj, level, fill_value)\u001b[0m\n\u001b[0;32m    458\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_1d_only_ea_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    459\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0m_unstack_extension_series\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 460\u001b[1;33m         unstacker = _Unstacker(\n\u001b[0m\u001b[0;32m    461\u001b[0m             \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconstructor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor_expanddim\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    462\u001b[0m         )\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\pandas\\core\\reshape\\reshape.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, index, level, constructor)\u001b[0m\n\u001b[0;32m    131\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Unstacked DataFrame is too big, causing int32 overflow\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 133\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_selectors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    134\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    135\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mcache_readonly\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\pandas\\core\\reshape\\reshape.py\u001b[0m in \u001b[0;36m_make_selectors\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    183\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    184\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 185\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Index contains duplicate entries, cannot reshape\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    186\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    187\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroup_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcomp_index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Index contains duplicate entries, cannot reshape"
     ]
    }
   ],
   "source": [
    "df_e.pivot(index='foo', columns='bar', values='baz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th>baz</th>\n",
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       "      <th>0</th>\n",
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       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>two</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>three</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>four</td>\n",
       "      <td>D</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "     foo bar  baz\n",
       "0    one   A    1\n",
       "1    two   B    2\n",
       "2  three   C    3\n",
       "3   four   D    4"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"foo\": ['one', 'two', 'three', 'four'],\n",
    "                   \"bar\": ['A', 'B', 'C', 'D'],\n",
    "                   \"baz\": [1, 2, 3, 4]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">baz</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <th>four</th>\n",
       "      <th>one</th>\n",
       "      <th>three</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     baz                \n",
       "foo four  one three  two\n",
       "bar                     \n",
       "A    NaN  1.0   NaN  NaN\n",
       "B    NaN  NaN   NaN  2.0\n",
       "C    NaN  NaN   3.0  NaN\n",
       "D    4.0  NaN   NaN  NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot(index='bar', columns='foo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">baz</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       baz               \n",
       "bar      A    B    C    D\n",
       "foo                      \n",
       "four   NaN  NaN  NaN  4.0\n",
       "one    1.0  NaN  NaN  NaN\n",
       "three  NaN  NaN  3.0  NaN\n",
       "two    NaN  2.0  NaN  NaN"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot(index='foo', columns='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>foo</th>\n",
       "      <th>bar</th>\n",
       "      <th>baz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>two</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>three</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>four</td>\n",
       "      <td>D</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     foo bar  baz\n",
       "0    one   A    1\n",
       "1    two   B    2\n",
       "2  three   C    3\n",
       "3   four   D    4"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "foo    bar\n",
       "four   D      4.0\n",
       "one    A      1.0\n",
       "three  C      3.0\n",
       "two    B      2.0\n",
       "Name: baz, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['foo', 'bar'])['baz'].aggregate('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>bar</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "bar      A    B    C    D\n",
       "foo                      \n",
       "four   NaN  NaN  NaN  4.0\n",
       "one    1.0  NaN  NaN  NaN\n",
       "three  NaN  NaN  3.0  NaN\n",
       "two    NaN  2.0  NaN  NaN"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['foo', 'bar'])['baz'].aggregate('mean').unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "foo    bar\n",
       "four   D      1\n",
       "one    A      1\n",
       "three  C      1\n",
       "two    B      1\n",
       "Name: baz, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['foo', 'bar'])['baz'].aggregate('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>bar</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "bar      A    B    C    D\n",
       "foo                      \n",
       "four   NaN  NaN  NaN  1.0\n",
       "one    1.0  NaN  NaN  NaN\n",
       "three  NaN  NaN  1.0  NaN\n",
       "two    NaN  1.0  NaN  NaN"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['foo', 'bar'])['baz'].aggregate('count').unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Exercise 1\n",
    "\n",
    "Fix `df_e` by taking the maximum `baz` for `foo`, `bar` pairs (resolve conflicts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2\n",
    "\n",
    "Add to the following DataFrame:\n",
    "    \n",
    "`df = pd.DataFrame(np.random.randint(1,100, 80).reshape(8, 10))`\n",
    "\n",
    "Two elements:\n",
    "    \n",
    "- a column with a row-wise sum\n",
    "- a row with column-wise sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3\n",
    "\n",
    "Transform this DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weekday</th>\n",
       "      <th>Person 1</th>\n",
       "      <th>Person 2</th>\n",
       "      <th>Person 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Friday</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     weekday  Person 1  Person 2  Person 3\n",
       "0     Monday        12        10         8\n",
       "1    Tuesday         6         6         5\n",
       "2  Wednesday         5        11         7\n",
       "3   Thursday         8         5         3\n",
       "4     Friday        11         8         7\n",
       "5   Saturday         6         9        11\n",
       "6     Sunday         4        12        15"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'weekday': [\"Monday\", \"Tuesday\", \"Wednesday\", \n",
    "         \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"],\n",
    "        'Person 1': [12, 6, 5, 8, 11, 6, 4],\n",
    "        'Person 2': [10, 6, 11, 5, 8, 9, 12],\n",
    "        'Person 3': [8, 5, 7, 3, 7, 11, 15]}\n",
    "df = pd.DataFrame(data, columns=['weekday',\n",
    "        'Person 1', 'Person 2', 'Person 3'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... into a \"tidy\" DataFrame (1 row per data point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 4\n",
    "\n",
    "Considering this DataFrame:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Google</th>\n",
       "      <th>Apple</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MON</td>\n",
       "      <td>1129</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TUE</td>\n",
       "      <td>1132</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WED</td>\n",
       "      <td>1134</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>THU</td>\n",
       "      <td>1152</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FRI</td>\n",
       "      <td>1152</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Day  Google  Apple\n",
       "0  MON    1129    191\n",
       "1  TUE    1132    192\n",
       "2  WED    1134    190\n",
       "3  THU    1152    190\n",
       "4  FRI    1152    188"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data = {\n",
    "    'Day' : ['MON', 'TUE', 'WED', 'THU', 'FRI'], \n",
    "    'Google' : [1129,1132,1134,1152,1152], \n",
    "    'Apple' : [191,192,190,190,188] \n",
    "})\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Company</th>\n",
       "      <th>Closing Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MON</td>\n",
       "      <td>Google</td>\n",
       "      <td>1129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TUE</td>\n",
       "      <td>Google</td>\n",
       "      <td>1132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WED</td>\n",
       "      <td>Google</td>\n",
       "      <td>1134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>THU</td>\n",
       "      <td>Google</td>\n",
       "      <td>1152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FRI</td>\n",
       "      <td>Google</td>\n",
       "      <td>1152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MON</td>\n",
       "      <td>Apple</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>TUE</td>\n",
       "      <td>Apple</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>WED</td>\n",
       "      <td>Apple</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>THU</td>\n",
       "      <td>Apple</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FRI</td>\n",
       "      <td>Apple</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Day Company  Closing Price\n",
       "0  MON  Google           1129\n",
       "1  TUE  Google           1132\n",
       "2  WED  Google           1134\n",
       "3  THU  Google           1152\n",
       "4  FRI  Google           1152\n",
       "5  MON   Apple            191\n",
       "6  TUE   Apple            192\n",
       "7  WED   Apple            190\n",
       "8  THU   Apple            190\n",
       "9  FRI   Apple            188"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reshaped_df = df.melt(id_vars=['Day'], var_name='Company', value_name='Closing Price')\n",
    "reshaped_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Turn `reshaped_df` back into `df` using `pivot` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[main 3d1df0c] Updates\n",
      " 1 file changed, 14 insertions(+), 3 deletions(-)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "warning: LF will be replaced by CRLF in 03.Data_Wrangling_1of3.ipynb.\n",
      "The file will have its original line endings in your working directory\n"
     ]
    }
   ],
   "source": [
    "!git commit -am \"Updates\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "To github.com:MichalKorzycki/PythonDataScienceEN.git\n",
      "   737df92..3d1df0c  main -> main\n"
     ]
    }
   ],
   "source": [
    "!git push"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Already up to date.\n"
     ]
    }
   ],
   "source": [
    "!git pull"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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